Posts from June 2017

Thursday, June 15, 2017

Crossposted on the Google Research Blog
At Google, we develop flexible state-of-the-art machine learning (ML) systems for computer vision that not only can be used to improve our products and services, but also spur progress in the research community. Creating accurate ML models capable of localizing and identifying multiple objects in a single image remains a core challenge in the field, and we invest a significant amount of time training and experimenting with these systems.

Today we are happy to make this system available to the broader research community via the TensorFlow Object Detection API. This codebase is an open source framework built on top of TensorFlow that makes it easy to construct, train and deploy object detection models. Our goals in designing this system was to support state-of-the-art models while allowing for rapid exploration and research. Our first release contains the following:

Frozen weights (trained on the COCO dataset) for each of the above models to be used for out-of-the-box inference purposes.

A Jupyter notebook for performing out-of-the-box inference with one of our released models

Convenient local training scripts as well as distributed training and evaluation pipelines via Google Cloud

The SSD models that use MobileNet are lightweight, so that they can be comfortably run in real time on mobile devices. Our winning COCO submission in 2016 used an ensemble of the Faster RCNN models, which are are more computationally intensive but significantly more accurate. For more details on the performance of these models, see our CVPR 2017 paper.

Are you ready to get started?
We’ve certainly found this code to be useful for our computer vision needs, and we hope that you will as well. Contributions to the codebase are welcome and please stay tuned for our own further updates to the framework. To get started, download the code here and try detecting objects in some of your own images using the Jupyter notebook, or training your own pet detector on Cloud ML engine!

By Jonathan Huang, Research Scientist and Vivek Rathod, Software EngineerAcknowledgements
The release of the Tensorflow Object Detection API and the pre-trained model zoo has been the result of widespread collaboration among Google researchers with feedback and testing from product groups. In particular we want to highlight the contributions of the following individuals:

Wednesday, June 14, 2017

Deep learning has fueled tremendous progress in the field of computer vision in recent years, with neural networks repeatedly pushing the frontier of visual recognition technology. While many of those technologies such as object, landmark, logo and text recognition are provided for internet-connected devices through the Cloud Vision API, we believe that the ever-increasing computational power of mobile devices can enable the delivery of these technologies into the hands of our users, anytime, anywhere, regardless of internet connection. However, visual recognition for on device and embedded applications poses many challenges — models must run quickly with high accuracy in a resource-constrained environment making use of limited computation, power and space.

Today we are pleased to announce the release of MobileNets, a family of mobile-first computer vision models for TensorFlow, designed to effectively maximize accuracy while being mindful of the restricted resources for an on-device or embedded application. MobileNets are small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models, such as Inception, are used.

Example use cases include detection, fine-grain classification, attributes and geo-localization.

This release contains the model definition for MobileNets in TensorFlow using TF-Slim, as well as 16 pre-trained ImageNet classification checkpoints for use in mobile projects of all sizes. The models can be run efficiently on mobile devices with TensorFlow Mobile.

Choose the right MobileNet model to fit your latency and size budget. The size of the network in memory and on disk is proportional to the number of parameters. The latency and power usage of the network scales with the number of Multiply-Accumulates (MACs) which measures the number of fused Multiplication and Addition operations. Top-1 and Top-5 accuracies are measured on the ILSVRC dataset.

Tuesday, June 6, 2017

Now that Google Summer of Code (GSoC) 2017 is under way with students in their first full week of the coding period we wanted to bring you some more statistics on the 2017 program. Lots and lots of numbers follow:

Organizations

Students are working with 201 organizations (the most we’ve ever had!) of which 39 are participating in GSoC for the first time.

Student Registrations

Over 20,651 students from 144 countries registered for the program, which is an 8.8% increase over the previous high for the program.

Project Proposals

4,764 students from 108 countries submitted a total of 7,089 project proposals.

Gender breakdown

11.4% of accepted students are women. We are always interested in making our programs and open source more inclusive. Please contact us if you know of organizations we should work with to spread the word about GSoC to underrepresented groups.

Universities

The 1,318 students accepted into the GSoC 2017 program hailed from 575 universities, of which 142 have students participating for the first time in GSoC.